ArticlePerspectives in health information managemen2021
An Interdisciplinary Approach to Reducing Errors in Extracted Electronic Health Record Data for Research.
Article in Perspectives in health information managemen, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Machine Learning for Mental Health: Applications, Challenges, and the Clinician's Role.Current psychiatry reports · 2024Review
- Optimization of Electronic Health Record Usability Through a Department-Led Quality Improvement Process.Annals of family medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Erroneous electronic health record (EHR) data capture is a barrier to preserving data integrity. We assessed the impact of an interdisciplinary process in minimizing EHR data loss from prescription orders. We implemented a three-step approach to reduce data loss due to missing medication doses: Step 1-A data analyst updated the request code to optimize data capture; Step 2-A pharmacist and physician identified variations in EHR prescription workflows; and Step 3-The clinician team determined daily doses for patients with multiple prescriptions in the same encounter. The initial report contained 1421 prescriptions, with 377 (26.5 percent) missing dosages. Missing dosages reduced to 361 (26.3 percent) prescriptions following Step 1, and twenty-three (1.7 percent) records after Step 2. After Step 3, 1210 prescriptions remained, including 16 (1.3 percent) prescriptions missing doses. Prescription data is susceptible to missing values due to multiple data capture workflows. Our approach minimized data loss, improving its validity in retrospective research.
Indexed as
Identifiers
34035787PMC8120677What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.